Best Machine Learning Tools & Frameworks for Mobiles

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Some of the most interesting new mobile applications from 2017 included machine learning and artificial intelligence in new and exciting ways. And with all the major tech houses such as Google, Microsoft, Adobe, Apple and Tencent all pouring large amounts of money into research and development in this area, 2017 is looking to become the start of a new era of mobile intelligence.

This has happened due to a number of reasons, but one of the most important ones is the fact that the hardware in smartphones and tablets have become so powerful that the small devices can run advanced algorithms locally, instead of having to use the internet and request servers to do the heavy lifting in terms of calculations.

Another important reason for the large amount of new ML enhanced applications has to do with the impressive number and quality of machine learning tools and frameworks for mobiles that is designed to be lighter and faster than the traditional tools built for fast desktop computers.

For now, let’s have a look at some of the best frameworks and toolkits for developing machine learning enhanced apps with the current technology we have available.

Best Machine Learning Tools & Frameworks

TensorFlow

TensorFlow

As with many of Google’s services and products, TensorFlow has quickly become the most popular free framework out there, and for good reason. Not only is the entire developer team competent and backed by Google’s resources, but the community has taken to TensorFlow with a great liking, and is very actively supporting new users, fixing bugs and adding modules and plugins to further enhance this popular framework.

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CoreML

CoreML

For iPhone and iOS developers, Apple’s own framework for building machine learning applications is almost as popular as TensorFlow. Built specifically for being blazingly fast for smaller devices, CoreML makes use of pretrained data to help speed up the performance locally, and with offline functionality built-in. Some of their most famous apps such as Siri and Quicktype are built with CoreML features, and more are being built every day now that private developers can use all of Apple’s tools.

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Nervana Neon

Nervana Neon

This is Intel’s GPU based deep-learning library. While not built specifically for mobile development, it works very well with never smartphones and tablets that have good graphics processing built-in. Nvidia and Intel have been working on Neon to specifically get some of the best performance possible for a deep learning tool such as this, and already people are building great apps with this framework.

Caffe2

Caffe2

This is one of the newest frameworks on our list, and a very promising one at that. Built using a modular architecture, developers can pick and choose between the different functions they need, and only choose the most important ones to ensure no extra bloat occurs. Furthermore, it’s possible to either choose a cloud-based setup, or run every calculation locally on the phone. Caffe2 also includes out-of-the-box integrations for setting up both Android, iOS, Windows, Linux and other operating systems painlessly.

Weka 3

Weka 3

Designed with Java, Weka is an interesting framework for a number of reasons. With the option of either using the pre-built algorithms to run data-mining operations, or the option of using the algorithms in custom Java applications, developers are not locked to using one system or another. A number of great tutorials showcase how to install Weka 3 with node package manager, and includes step by step instructions for how to integrate MongoDB and various web frameworks.

Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit

From the developers of Windows comes their cognitive framework, capable of running deep learning calculations on all devices, including Apple’s with the right setup. With a large set of different builds it is possible to run CNTK on either the GPU or CPU, and a large number of operating systems are supported as well. Using directed graphs for all purposes, CNTK is a great way to start developing with machine learning.

Conclusion

So, the combination of better hardware, and better software has led us to the point where we can now talk to our phones and they can understand us in real-time, without necessarily using the internet or other 3rd party services. And with CPU & GPU chips becoming cheaper and faster for every year that passes, only time will tell how advanced we can make our mobile applications in the future.

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About Author

David James is co-founder at TechJeny. He writes about Digital Marketing, WordPress, SEO, Web Design, Development, Technology, Apps, Tips and Tricks. For more info contact us here. For Advertise on TechJeny, visit Advertise page.

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